Visualizations with R

We will go over visualizations with the functions available with base R, as well as the ggplot package. In general base R is good for quick visualizations, but for most public purposes, PPTs and publications, you will want to use a visualization package. A common package is ggplot, so that is what we will teach you here.

Plotting in base R

The basic plotting function is plot(), which takes a formula plot(y ~ x) or explicit definitions of the x and y axis plot(x= , y= ). The inputs can be vectors or columns in a data frame. If columns in a data frame, then the data frame must also be defined, plot(y ~ x, data= ?). Let’s make some plots with CO2 data frame, which shows different CO2 uptake rates for plants.

head(CO2)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8
## 4   Qn1 Quebec nonchilled  350   37.2
## 5   Qn1 Quebec nonchilled  500   35.3
## 6   Qn1 Quebec nonchilled  675   39.2

#  Plot of uptake vs. CO2 concentration
plot(uptake ~ conc, data= CO2)

#  Add lines to the points
plot(uptake ~ conc, data= CO2[which(CO2$Plant == "Qn1"), ])
lines(uptake ~ conc, data= CO2[which(CO2$Plant == "Qn1"), ])

# A boxplot of the same data
boxplot(uptake ~ conc, data= CO2)

# For customizations start with par
?par
boxplot(uptake ~ conc, col= "red", data= CO2)

Histograms

# Length of eruptions and time between eruptions at Old Faithful geyser
head(faithful)

# Make a plot of eruptions vs. waiting

# Histogram of eruptions
hist(faithful$eruptions, breaks= 20)

More complex objects can also be plotted using plot and many package developers will use plot to work with different R objects.

# We can make a linear regression of our eruptions vs. waiting
lm.faith <- lm(eruptions ~ waiting, data= faithful)
lm.faith

## 
## Call:
## lm(formula = eruptions ~ waiting, data = faithful)
## 
## Coefficients:
## (Intercept)      waiting  
##    -1.87402      0.07563

# Automatically generates 4 plots for evaluation of the model
  # plot(lm.faith)

# We can then add the regression line to our scatterplot
plot(eruptions ~ waiting, data= faithful)
abline(lm.faith)

Phylogenetic Trees

# install.packages("ape")
library(ape)

data(bird.orders)
plot(bird.orders)

ggplot: the grammar of graphics

library(ggplot2)

ggplot works like a language, but instead of words you, string together different layers to build your plot. Just like any language ggplot has a grammar to make it interpretable.

Before we can plot the data we need to go over a few terms.

Geoms These are the actual marks that are placed on the plot
- points, lines, bars, boxplots, etc.

Aesthetics These define the visual qualities (i.e. aesthetics) of the geoms on the plot
- position, size, color, fill, linetype.

We will start with the Orange dataset.

head(Orange)

# install.packages(ggplot2)
# library(ggplot2)

Building a ggplot

We will use the Orange data set in base R. Which shows the circumference of 5 orange trees at different ages.

Let’s plot the circumference of trees against their age.
We start by using ggplot() to define the data and how it will be positioned
aes() stands for aesthetics

ggplot(data = Orange, aes(x = age, y = circumference))

By defining our data and positions, we have created the foundational plot upon which we can now add our layers. You can see below there are no points, because we have not specified any geoms.

We add additional layers using the + sign.
We need to add a geom, so that we can place some actual marks on our plot.

Geoms all take the form geom_xxx
- geom_point
- geom_line
- geom_bar
- geom_boxplot
- etc.

ggplot(data = Orange, aes(x = age, y = circumference)) + geom_point()

But we have no idea which points correspond to which trees. Let’s assign each tree a different color in a new plot.

ggplot(data = Orange, aes(x = age, y = circumference)) + 
  geom_point(aes(color= Tree))

Notice how I can have my layers be on a separates line? Placing each layer on a separate line can increase the readibility of your code

Variables it is possible to use variables to save time while making a ggplot. I can save my base ggplo() function to a variable, so that I do not have to keep typing it as I make different plots.

orange.plot <- ggplot(data = Orange, aes(x = age, y = circumference))

What happens when you run these commands?

# 1
orange.plot + 
  geom_point(color= Tree)

# 2
orange.plot + 
  geom_point(color= "purple")

Anytime you specify an aesthetic characteristic outside the aes() command, ggplot will apply that quality to all marks in that particular geom. The size of points is something that we usually want to apply globally, but sometimes we might want it to relate to the variables.

# Remember orange.plot <- ggplot(data = Orange, aes(x = age, y = circumference))

# 1
orange.plot + 
  geom_point(aes(color= Tree, size= Tree))

# 2
orange.plot + 
  geom_point(aes(color= Tree), size= 3)

# 3 
orange.plot + 
  geom_point(aes(color= Tree, size= 3))

We can continue to build our plot with more layers. Let’s add lines to connect the points that belong to each tree. Let’s make the size of our points so they are easier to see too.

orange.plot + 
  geom_point(aes(color= Tree), size= 3) +
  geom_line()

Oops, that did not work, ggplot connected all the points with one line, because we never told ggplot we wanted the points grouped together so that only points belonging to the same tree would be connected. To do this we use the group command.

orange.plot.group <- ggplot(data = Orange, aes(x = age, 
                                               y = circumference, 
                                               group= Tree))

orange.plot.group + 
  geom_point(aes(color= Tree), size= 3) +
  geom_line()

Much better What happens if you switch the order of the geom_point() and geom_line() layers? How could we get the color of the lines to match it’s Tree?

Building your own plot

Check out the ‘iris’ dataset. This is a dataset of leaf measurements for 3 different species of iris. Build a scatter plot (geom_point) of y= Sepal.Width aand x= Sepal.Length with each species being a different color and shape and change the size of the points to make the plot easier to read. (Hint: shape is another aesthetic quality and functions just like color and size).

ggplot(data= iris, aes(x= Sepal.Length, y= Sepal.Width)) + 
  geom_point(aes(color= Species, shape= Species), size= 3)

Now we will see how to use the same data and make a more complicated plot. Here will be our end result, so lets work through how to build this figure.

Adding trendlines

geom_smooth will plot linear regression lines on your data, as well as non-linear smoothing lines. This is a helpful way to aid your eye in seeing the patterns in your data, think of these as qualitative. They should not be a substitute for a formal statistical test.

Arguments for geom_smooth(aes(), method= ?, se= ?, alpha= ?)
- method: smoothing method (we will use "lm"= linear model)
- se: should standard errors be plotted (logical T/F)
- alpha: controls the transparency of a filled region (0-1)
- size: similar to ‘geom_point’ size controls the width of the line

Task 1

Use geom_smooth() to add trend lines to our previous plot to create the following plot.
The color of the line and the std. error ribbon should match the points
Decrease the transparency, so it is similar to the following plot

 ggplot(data= iris, aes(x= Sepal.Length, y= Sepal.Width)) + 
  geom_point(aes(color= Species, shape= Species), size= 3) + 
  geom_smooth(aes(color= Species, fill= Species, group= Species), 
              size= 0.75,  
              method= "lm", 
              alpha= 0.2)

Customization using `scales`

This is our first introduction to ggplots use of scales. Scales exemplify the grammar of graphics. You will see a set of rules and conventions consistently applied to a variety of commands and functions.
Scales associate values in your data to values within a given aesthetic characteristic (e.g. shape, color, etc.) Your data is most likely either continuous or discrete, and aesthetics are also continuous or discrete.

Scales have a constant naming convention:

first scale_,
then the name of the aesthetic scale_color,
then type of data scale_color_discrete()
This applies to all aesthetics: shape, color, fill, size, x, y, linetype

There are a few arguments common to all scales:

name: the label that will appear on the axis or legend
limits: set the domain of the scale (what appears on the plot)
- continuous scales take a max and min value
- discrete scales take a character vector
- if you set limits smaller than the data range, then values outside the limits will be dropped
breaks: what will appear on the axis or legend
- how tick marks will appear on an axis or how aesthetics will be segmented
labels: the label that appears at each breakpoint

Legends display the aesthetics specified by the scales.

legends are also referred to as guides
Legends consist of:
- Title: at the top
- Key: display of aesthetic (line, point, etc.)
- Key label: variable that aesthetic represents
ggplot will automatically create the legend
- if the same variables are used with more than one aesthetic, then ggplot will merge the legends together
the breaks argument in scale_xx_xx() commands determine how to draw the keys
All merged legends must have the same name in the scale_xx_xx() command

# Breaks= what  appears on the axis or legend
ggplot(data= iris, aes(x= Sepal.Length, y= Sepal.Width)) +
  geom_point(aes(color= Species)) +
  scale_x_continuous(breaks= seq(4,8, by= 0.5)) +
  scale_color_hue(name= "Iris Species", breaks= "setosa")

# Limits= what appears on the plot
ggplot(data= iris, aes(x= Sepal.Length, y= Sepal.Width)) +
  geom_point(aes(color= Species)) +
  scale_x_continuous(limits= c(4, 6)) +
  scale_color_hue(name= "Iris Species", limits= "setosa")

## Warning: Removed 61 rows containing missing values (geom_point).

There is also an option to make manual scales scale_color_manual, which allows you to create your own discrete scale using the values argument.
scale_color_manual(values= c("white", "orange", "purple"))
Shapes can also be customized using scale_shape_manual

type ?pch to get the help document for shapes
ggplot will map up to 6 shapes, any more need to be coded manually

Task 2

Use scale_xxx_manual commands to adjust the colors to match the black, red, and blue of the following plot. The point, lines, and std. error ribbon should all be the same colors.
Change the shapes of the points
Use the ‘names’ argument to change the legend title to also match the plot

Variables and ggplot

Making variables for your ggplots can save time and help you organize your plots.

It is common for people to assign their ggplot() command to a variable, then add layers onto that variable.
You can also assign information that will be used on multiple layers in your plots to a variable, then when you need to change that information. You can change your variable once, rather than changing the information on each layer.
If a layer has many arguments and is becoming long, you can split it up over multiple lines. This will increase the readibility of your code. Hitting cmd-i, will auto align the lines.

iris.plot <- ggplot(data= iris, aes(x= Sepal.Length, y= Sepal.Width))
species.colors <- c("black", "red", "blue")
species.shapes <- c(15, 2, 8)
legend.title <- "Iris Species"

iris.plot + 
  geom_point(aes(color= Species, shape= Species), size= 3) + 
  geom_smooth(aes(color= Species, fill= Species, group= Species), 
              size= 0.75,  method= "lm", alpha= 0.2) + 
  scale_color_manual(name= "Iris Species", values= c("black", "red", "blue")) +
  scale_fill_manual(name= legend.title, values= species.colors) +
  scale_shape_manual(name= legend.title, values= species.shapes)

Task 3
Use recode your ggplot with variables to simplify and streamline your ggplot

#### Customizing axes

Axis components include:

name: title of the axis
breaks and minor-breaks (tick marks)
labels: identity of each tick mark
limits: range of the axis

Customizing axes is possible using scales_xx_xx() commands

scale_x_continuous(name= "?", breaks= c(?), minor_breaks= c(?), labels= c("?"), limits= c(min, max))

Other commands are also available if you just want to modify some specifics
labs(x= "?", y="?"): modify the axis names
ylim(min, max): modify only the y axis limits
xlim(min, max): modify only the x axis limits

Task 4 Update the axis name to match the plot below

Themes

To ggplot, themes are all the non-data elements of a plot. And consist of two components:

theme elements: individual attributes of the plot that are independent of the data
- axis.text, legend.background, panel.grid, etc.
element functions: the commands that enable you to modify different theme elements
- element_text(): labels and headings
- element_line(): lines, tick marks, grid lines
- element_rect(): rectangles, backgrounds, legend keys
- element_blank(): draw nothing, used to disable theme elements

The theme is modified using a nested syntax with the theme() function. You can then string together as many theme elements as you desire, each one separated by a comma.
theme(axis.text = element_text(size= 20), panel.background = element_rect(color= "white")) There are also several pre-loaded theme functions that can be called

theme_grey() (the default theme)
theme_bw(): similar to theme_grey but with a white background
theme_classic(): no grid lines
and more

It is possible to call an pre-loaded theme and then alter it further by adding an additional theme() layer.

myplot + theme_bw() + theme(legend.position = "top")

You can also save your own custom themes as their own variable, then add the new variable to your ggplot

# Create custom theme
my.theme <- theme(axis.text = element_text(size= 20), 
                  panel.background = element_rect(color= "white"))

# Add new theme to plot
my.plot + geom_point() + my.theme

The command base_size is a quick convenient way to increase the font size of all text in your plots. This is helpful in preparing figures for PPT presentations
myplot + theme_bw(base_size= 22)

There are many theme elements and the ggplot website has excellent documentation for modifying themes:
http://docs.ggplot2.org/dev/vignettes/themes.html

Task 5 Change the theme from the default theme to match the plot below. After each step below, re-generate the plot so you can see how the theme changes.

add theme_bw(base_size = 20) to your plot
after theme_bw() add theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
inside the theme() function from step 2, add legend.position = c(0.88, 0.88), legend.background = element_rect(color = "black") after the panel.grid elements

Now the plot looks quite different. There are many different ways to change themes, now that you are familiar with the basic syntax your options are limitless!

We are finished!

Your code and plot should now look something like this:

# Define data and positionings
iris.plot <- ggplot(data= iris, aes(x= Sepal.Length, y= Sepal.Width))

# Save aesthetic qualities as variables
species.colors <- c("black", "red", "blue")
species.shapes <- c(15, 2, 8)
legend.title <- "Iris Species"

# Generate the plot by combining layers
iris.plot + 
  geom_point(aes(color= Species, shape= Species), size= 3) + 
  geom_smooth(aes(color= Species, fill= Species, group= Species), 
              size= 0.75,  method= "lm", alpha= 0.2) + 
  labs(x= "Sepal Length (cm)", y= "Sepal Width (cm)") +
  scale_color_manual(name= "Iris Species", values= c("black", "red", "blue")) +
  scale_fill_manual(name= legend.title, values= species.colors) +
  scale_shape_manual(name= legend.title, values= species.shapes) + 
  theme_bw(base_size= 20) + 
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        legend.position = c(0.85, 0.85), 
        legend.background = element_rect(color = "black"))

ggplot vs. base R graphics

Here is that same plot generated with base R graphics. Way more code and it still doesn’t look as good. (I eventually got tired of figuring out how to further customize the plot)

# Save aesthetics as variables
cols <- c("black", "red", "blue")
cols_species <- cols[iris$Species]
pch_species <- as.numeric(iris$Species)

# Create the plot
plot(Sepal.Width ~ Sepal.Length, data= iris, col= cols_species, pch= pch_species, cex= 1.5, xlab= "Sepal Length (cm)", ylab= "Sepal Width (cm)", cex.lab= 1.5)

# Create the legend
legend(x= 4, y= 5, title= "Iris Species", 
       legend=c("setosa", "versicolor", "virginica"), 
       col= cols, pch= as.numeric(iris$Species), 
       cex= 0.8, pt.cex= 0.8, 
       bty= "n", xpd= TRUE, y.intersp= 0.5, title.adj= 0, horiz= T)

# Run linear models to get regression coefficients
fit.setosa <- lm(Sepal.Width ~ Sepal.Length, 
                 data= iris[which(iris$Species == "setosa"), ])
fit.versicolor <- lm(Sepal.Width ~ Sepal.Length, 
                 data= iris[which(iris$Species == "versicolor"), ])
fit.virginica <- lm(Sepal.Width ~ Sepal.Length, 
                 data= iris[which(iris$Species == "virginica"), ])

# Add trend lines to the plot
abline(fit.setosa, col= "black", lwd= 2)
abline(fit.versicolor, col= "red", lwd= 2)
abline(fit.virginica, col= "blue", lwd= 2)

Saving plots

Arguments for ggsave()

plot name: an R variable where your plot is saved
filename: for newly created file
- file will be deposited in your working directory
- alternatively you can provide an entire file path to save outside your wd using the path argument
device: the type of file to be saved (.pdf, .jpg, .png., etc.)
- alternatively you can specify the extension in the filename
width and height: numeric dimensions for the plot
units: specify the units for the width and height arguments
- “cm”, “in”, “mm”

ggsave(our.plot, "RWorkshopPlot.pdf", width= 3, height= 3, units= "in")

You can also use the export tab in the RStudio graphics window to save plots

Other types of plots with ggplot

Faceting

Ggplot allows you to break up your data into differen subsets and then plot these subsets together. This called faceting, and is also known as latticing. This is a powerful way to detect patterns among subsets of your data, however it is designed to be used on subsets that share a common scale.

There are two ggplot functions to generate facets: facet_grid()and facet_wrap()

# We could facet our iris data rather than use colors/shapes to differentiate
# the different species
iris.plot + 
  geom_point() + 
  facet_grid(. ~ Species) + 
  theme_bw(base_size = 20)

facet_grid arguments
- Creates a 2-dimensional grid of facets based on the variables you assign
- Uses a formula to specify the variables to be subset with a . as a placeholder
- facet_grid(. ~ Species): single row, Species variable as multiple columns
- facet_grid(Species ~ .): single column, Species variable as multiple rows
- facet_grid(Species ~ another variable): Species as rows, another variable as columns
facet_wrap arguments
- takes a 1d vector and wraps it into 2 dimensions.
- This is helpful if you have a variable with many levels and you want to specify the number of rows and columns in your plot
- facet_wrap(~ Species, nrow= ?, ncol= ?): you specify a single variable, always preceded by a ~, then define the rows and columns for the wrap.

To practice faceting we will use fuel efficency data (mpg3) from the EPA based on number of cylinders (cyl) in the car and the type of drive train (drv) front wheel drive ("f") and 4wd ("4").

## 'data.frame':    205 obs. of  5 variables:
##  $ year: int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl : Factor w/ 3 levels "4","6","8": 1 1 1 1 2 2 2 1 1 1 ...
##  $ drv : Factor w/ 2 levels "4","f": 2 2 2 2 2 2 2 1 1 1 ...
##  $ cty : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy : int  29 29 31 30 26 26 27 26 25 28 ...

Task 6
You will make some plots using mpg3 to practice faceting.

First, make a scatter plot with cty on the x-axis and hwy on the y and cyl as a different shape and drv as a different color. Your plot should look something like this.

Now plot each cyl and drv as different facets using facet_grid(). Explore different combinations of rows and columns, but eventually finish by faceting cyl as 3 columns.
Add the argument scales= "free_y" to facet_grid(). What happened?
Change the background of each facet label using theme()

Facet labels are called strips. Use theme(strip.background = element_rect()) to change the color and fill. Pick any color combinations you like.

Remove the cyl legend using the guide= FALSE argument inside a scale_xx_xx function. You need to figure out which scale function to use.

Your plot should look something like this:

mpg.plot <- ggplot(mpg3, aes(x= cty, y= hwy)) 
  
mpg.plot + 
  geom_point(aes(shape= cyl, color= drv), size = 3) + 
  facet_grid(cyl~., scales= "free_y") + 
  scale_shape_discrete(guide= FALSE) +
  theme_bw(base_size= 20) + 
  theme(strip.background = element_rect(fill = "white", color= "black"))

Bar plots and histograms

Bar plots are used for showing counts of things, the ggplot function is geom_bar()

When making a bar plot, geom_bar() can either plot the count of whatever variable you specify in the x-axis, or an actual value specified in the y-axis this is controlled using the stat argument

stat= "count": plots counts of the variable in the x-axis, the y value is left empty
- this is the default if stat is not specified
stat= "identity“: plots the value specified in the y-axis

# Here is data from 100 coin tosses
head(coin.flip)

##   result  face
## 1      1 Heads
## 2      0 Tails
## 3      0 Tails
## 4      0 Tails
## 5      1 Heads
## 6      1 Heads

# Bar plot
# Notice how only the x value is defined
bar.plot1 <- ggplot(data= coin.flip, aes(x= face))

bar.plot1 + geom_bar(stat= "count") + theme_bw(base_size= 22)

# Notice when stat is not specified it defaults to bin.
bar.plot1 + geom_bar() + theme_bw(base_size= 22)

In other situations we may know the count of the data, and we want to feed it directly to ggplot, in that case we also define a y value

head(coin.flip.summary)

##    face count
## 1 Heads    49
## 2 Tails    51

# Stat= identity
bar.plot2 <- ggplot(data= coin.flip.summary, aes(x= face, y= count))

bar.plot2 + 
  geom_bar(stat= "identity", fill= "dark blue") + 
  theme_bw(base_size= 22)

#If we specify stat=count, then the value is 1, because there is only one count of each level of the "face" variable
bar.plot3 <- ggplot(data= coin.flip.summary, aes(x= face))

bar.plot3 + 
  geom_bar(stat= "count", fill= "dark blue") + 
  theme_bw(base_size= 22)

Task 7 To explore geom_bar(), we will use the data set wpl, which has counts of the number of telephones in 7 regions of the world from 1951 to 1961. Spend the next few minutes generating a plot similar to the one below with:

Continent on the x-axis and telephone counts on the y-axis
Each year in a different color
Create a custom color scheme for each year (i.e. non-default colors)
Experiment making plots with different values for the position argument
- position takes values of "stacked", "dodge", "fill"
Rotate the x-axis labels using theme(axis.text.x = element_text(angle= ?), vjust = ?)

##   year continent phones
## 1 1951    N.Amer  45939
## 2 1956    N.Amer  60423
## 3 1957    N.Amer  64721
## 4 1958    N.Amer  68484
## 5 1959    N.Amer  71799
## 6 1960    N.Amer  76036

library(RColorBrewer)

wpl.bar.plot <- ggplot(data= wpl, aes(x= continent, y= phones))

wpl.bar.plot + 
  geom_bar(aes(fill= year), stat= "identity", position= "dodge") +
  scale_fill_brewer(palette= "Spectral") + 
  theme_bw(base_size = 22) + 
  theme(axis.text.x = element_text(angle= 45, vjust= 0.8))

Histograms can be made with geom_histogram defining only x-axis variable in aes(). The x-axis variable must be continuous, otherwise for discrete variables use geom_bar(stat= "count").

Task 8

Using the data set faithful, make a histogram of the wait times (waiting) between eruptions of the Old Faithful geyser in Yellowstone.
adjust the binwidth argument in geom_histogram()
customize the plot as you see fit

ggplot(data= faithful, aes(x= waiting)) + 
  geom_bar(stat= "bin", fill= "black", binwidth= 1) + 
  theme_bw(base_size= 20)

## Warning: `geom_bar()` no longer has a `binwidth` parameter. Please use
## `geom_histogram()` instead.

Plotting Errorbars

Error bars can be plotted with geom_errorbar. First you need to have a data frame where the min and max of the error bar are already calculated. In this example we will be plotting the standard error of chicken weights across 4 different diets.

head(chick.wt)

##   Diet  N mean.weight se.weight
## 1    1 16    177.7500  14.67552
## 2    2 10    214.7000  24.70944
## 3    3 10    270.3000  22.64904
## 4    4  9    238.5556  14.44925

To use geom_error bar first plot the mean value using a different geom_xx layer. Then you specify the range of the errorbars within the geom_errorbar(aes(ymin= ?, ymax= ?))layer.

errorbar.plot <- ggplot(data= chick.wt, aes(x= Diet, y= mean.weight))

errorbar.plot + 
  geom_point(size= 3) + 
  geom_errorbar(aes(ymin= mean.weight - se.weight, 
                    ymax= mean.weight + se.weight), width= 0.2) +
  theme_bw(base_size = 20)

Conclusion

This should give you a strong foundation to make plots using ggplot. You are now equipped to read the help documentation and explore additional ggplot capabilities.

Workshop_ggplot2

Keith Bouma-Gregson

1/5/2017

Visualizations with R

Plotting in base R

ggplot: the grammar of graphics

Building a ggplot

Building your own plot

Adding trendlines

Customization using `scales`

Variables and ggplot

Themes

We are finished!

Your code and plot should now look something like this:

ggplot vs. base R graphics

Saving plots

Other types of plots with ggplot

Faceting

Bar plots and histograms

Plotting Errorbars

Conclusion

Workshop_ggplot2

Keith Bouma-Gregson

1/5/2017

Visualizations with R

Plotting in base R

ggplot: the grammar of graphics

Building a ggplot

Building your own plot

Adding trendlines

Customization using scales

Variables and ggplot

Themes

We are finished!

Your code and plot should now look something like this:

ggplot vs. base R graphics

Saving plots

Other types of plots with ggplot

Faceting

Bar plots and histograms

Plotting Errorbars

Conclusion

Customization using `scales`